Cancer Detection and Classification Using CNN Model
Cancer Detection and Classification Using CNN Model |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-12 |
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| Year of Publication : 2024 | ||
| Author : Percy Okae, Theophilus Addo, Joseph Boateng Owusu-Afari, Gifty Bondzie |
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| DOI : 10.14445/22315381/IJETT-V72I12P104 | ||
How to Cite?
Percy Okae, Theophilus Addo, Joseph Boateng Owusu-Afari, Gifty Bondzie, "Cancer Detection and Classification Using CNN Model," International Journal of Engineering Trends and Technology, vol. 72, no. 12, pp. 42-54, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I12P104
Abstract
The research utilizes the CNN model to develop the machine learning mode due to its image extraction performance. The system was developed to identify and categorize eight (8) different kinds of cancers, namely lymphoma, oral cancer, brain cancer, breast cancer, cervical cancer, kidney cancer, lung and colon cancer, and leukemia. The multi cancer image dataset from Kaggle was utilized to train and test the models. The dataset contained eight (8) types of cancers grouped into different classes. For each class, 2000 images were used for training and 500 for testing. Pre-processing techniques were applied to normalize and standardize the images to ensure the correct format and resolution. Nine (9) CNN models were trained, with eight responsible for classifying each cancer type while the remaining model detects the cancer type. The system was designed to perform two levels of classification for each image. The first level is the detection of the type of cancer, and the second level is the classification of the cancer type. Generally, the manual examination of cancer diagnoses is error-prone, and this work sought to automate the process as best as possible by investigating the performance of the CNN model on selected types of cancer. The results demonstrated the effectiveness of the developed system in accurately detecting and classifying the eight types of cancers and the potential to alleviate the errors faced with the manual examination. All the models obtained accuracies above 90%.
Keywords
Cancer detection and classification, Convolutional Neural Network (CNN), Machine learning, Magnetic Resonance Imaging (MRI), Web application, Mobile application.
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